The performance and integration density of silicon integrated circuits (ICs) have progressed at an unprecedented pace in the past 60 years. While silicon ICs thrive at low‐power high‐performance computing, creating flexible and large‐area electronics using silicon remains a challenge. On the other hand, flexible and printed electronics use intrinsically flexible materials and printing techniques to manufacture compliant and large‐area electronics. Nonetheless, flexible electronics are not as efficient as silicon ICs for computation and signal communication. Flexible hybrid electronics (FHE) leverages the strengths of these two dissimilar technologies. It uses flexible and printed electronics where flexibility and scalability are required, i.e., for sensing and actuating, and silicon ICs for computation and communication purposes. Combining flexible electronics and silicon ICs yields a very powerful and versatile technology with a vast range of applications. Here, the fundamental building blocks of an FHE system, printed sensors and circuits, thinned silicon ICs, printed antennas, printed energy harvesting and storage modules, and printed displays, are discussed. Emerging application areas of FHE in wearable health, structural health, industrial, environmental, and agricultural sensing are reviewed. Overall, the recent progress, fabrication, application, and challenges, and an outlook, related to FHE are presented.

This article summarizes the Next Generation Attenuation (NGA) Subduction (NGA-Sub) project, a major research program to develop a database and ground motion models (GMMs) for subduction regions. A comprehensive database of subduction earthquakes recorded worldwide was developed. The database includes a total of 214,020 individual records from 1,880 subduction events, which is by far the largest database of all the NGA programs. As part of the NGA-Sub program, four GMMs were developed. Three of them are global subduction GMMs with adjustment factors for up to seven worldwide regions: Alaska, Cascadia, Central America and Mexico, Japan, New Zealand, South America, and Taiwan. The fourth GMM is a new Japan-specific model. The GMMs provide median predictions, and the associated aleatory variability, of RotD50 horizontal components of peak ground acceleration, peak ground velocity, and 5%-damped pseudo-spectral acceleration (PSA) at oscillator periods ranging from 0.01 to 10 s. Three GMMs also quantified “within-model” epistemic uncertainty of the median prediction, which is important in regions with sparse ground motion data, such as Cascadia. In addition, a damping scaling model was developed to scale the predicted 5%-damped PSA of horizontal components to other damping ratios ranging from 0.5% to 30%. The NGA-Sub flatfile, which was used for the development of the NGA-Sub GMMs, and the NGA-Sub GMMs coded on various software platforms, have been posted for public use.

This paper addresses the critical issue of localized damage detection in structural health monitoring. It evaluates the application of cumulative absolute velocity (CAV) as a local damage indicator for analyzing the acceleration response of a shaking table test specimen as well as real instrumented buildings. A bridge column test specimen instrumented with 18 accelerometers is subjected to increasing six levels of shaking corresponding to different damage states. The CAV analysis of the horizontal response of the accelerometers identified the elevation where damage is located. The vertical CAV analysis not only identified the existence of damage but also located the damage with high precision. This method is subsequently applied to two instrumented buildings damaged during earthquakes. The results show that for the seven-story Van Nuys hotel, the CAV can correctly locate the damage that occurred during the 1994 Northridge earthquake. For the Imperial county services building, the CAV is also able to correctly identify the location of damage. Thus, the presented CAV method enables identifying the onset and location of damage to evaluate the structural integrity.

Machine learning (ML)-aided structural health monitoring (SHM) can rapidly evaluate the safety and integrity of the aging infrastructure following an earthquake. The conventional damage features used in ML-based SHM methodologies face the curse of dimensionality. This paper introduces low dimensional, namely, cumulative absolute velocity (CAV)-based features, to enable the use of ML for rapid damage assessment. A computer experiment is performed to identify the appropriate features and the ML algorithm using data from a simulated single-degree-of-freedom system. A comparative analysis of five ML models (logistic regression (LR), ordinal logistic regression (OLR), artificial neural networks with 10 and 100 neurons (ANN10 and ANN100), and support vector machines (SVM)) is performed. Two test sets were used where Set-1 originated from the same distribution as the training set and Set-2 came from a different distribution. The results showed that the combination of the CAV and the relative CAV with respect to the linear response, i.e., RCAV, performed the best among the different feature combinations. Among the ML models, OLR showed good generalization capabilities when compared to SVM and ANN models. Subsequently, OLR is successfully applied to assess the damage of two numerical multi-degree of freedom (MDOF) models and an instrumented building with CAV and RCAV as features. For the MDOF models, the damage state was identified with accuracy ranging from 84% to 97% and the damage location was identified with accuracy ranging from 93% to 97.5%. The features and the OLR models successfully captured the damage information for the instrumented structure as well. The proposed methodology is capable of ensuring rapid decision-making and improving community resiliency.

This paper presents two on-going efforts of the Pacific Earthquake Engineering Research (PEER) center in the area of structural health monitoring. The first is data-driven damage assessment, which focuses on using data from instrumented buildings to compute the values of damage features. Using machine learning algorithms, these damage features are used for rapid identification of the level and location of damage after earthquakes. One of the damage features identified to be highly efficient is the cumulative absolute velocity. The second is vision-based automated damage identification and assessment from images. Deep learning techniques are used to conduct several identification tasks from images, examples of which are the structural component type, and level and type of damage. The objective is to use crowdsourcing, allowing the general public to take photographs of damage and upload them to a server where damage is automatically identified using deep learning algorithms. The paper also introduces PEER.s effort and preliminary results in engaging the engineering and computer science communities in such developments through the PEER Hub Image-Net (F-Net) challenge.